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Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas .〔 〕〔 〕 It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution and estimation of distribution algorithm. It is developed at Université Laval since 2009. == Example == The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP. import array, random from deap import creator, base, tools, algorithms creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) evalOneMax = lambda individual: (sum(individual),) toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.population(n=300) NGEN=40 for gen in range(NGEN): offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = offspring 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「DEAP (software)」の詳細全文を読む スポンサード リンク
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